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تأثیر مستندسازی نژادگان و معماری‌های مختلف ژنگانی بر عملکرد روش‌های جنگل تصادفی و بیز آستانه‌ای A در پیش‌بینی ژنگانی

نوع مقاله: مقاله پژوهشی

نویسنده

استادیار، دانشگاه آزاد اسلامی، واحد آستارا

چکیده

انتخاب ژنگانی (ژنومی) با بهره‌گیری از مستندسازی (ایمپیوتیشن) می‌تواند نقش مهمی در افزایش بهره‌وری اقتصادی و پیشرفت ژنتیکی صفات آستانه‌ای ایفا کند. هدف این تحقیق  بررسی درستی مستندسازی و تأثیر آن در سطح زیر منحنی مشخصۀ عملکرد (AUROC) ارزیابی ژنگانی روش‌های بیز آستانه‌ای  A(TBA) و جنگل تصادفی (RF) در ویژگی‌های آستانه‌ای با معماری‌های مختلف ژنگانی است. داده‌های ژنگانی برای سطوح متفاوت وراثت‌پذیری (1/0 و 3/0)، سطوح مختلف LD (135/0 و 295/0) و شمار متفاوت جایگاه­های ویژگی‌های کمی (108 و 1080) روی کروموزم 27 کروموزم همانند‌سازی شدند. برای همانندسازی شرایط واقعی برای هر پیش‌فرض (سناریو)، از بین 54 هزار نشانگر همانندسازی‌شده به‌طور تصادفی اقدام به حذف 50 درصد و 90 درصد نشانگرها کرده و در مرحلۀ بعد با مستندسازی اقدام به پیش‌بینی نژادگان (ژنوتیپ) نشانگرها کرده و درستی مستندسازی ارزیابی شد. در گام آخر، نژادگان‌های اصلی و مستند‌شده با استفاده از روش‌ TBA و RF برای ارزیابی AUROC استفاده شدند. با افزایش سطح LD و کاهش میزان حذف نشانگرها، درستی مستندسازی بهبود ­یافت. میانگین AUROC پیش‌فرض‌های همانندسازی‌شده برای جنگل تصادفی و TBA به­ترتیب 64/0 و 66/0 بود. استفاده از نژادگان‌های مستند‌شده با میزان حذف 50 درصد و 90 درصد، به­ترتیب AUROC را به میزان 013/0 و 02/0 برای RF و 018/0 و 026/0 برای TBA کاهش داد. به‌رغم AUROC بالای روش‌ بیز آستانه‌ای A در پیش‌فرض‌های مختلف، روش جنگل تصادفی عملکرد بهتری در شمار بالای QTL نشان داد. به‌طورکلی استفاده از نژادگان‌های مستند‌شده (k5) می‌تواند راهکار مهمی برای کاهش هزینه‌های ارزیابی ژنگانی باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Impact of genotype imputation and different genomic architectures on the performance of random forest and threshold Bayes A methods for genomic prediction

نویسنده [English]

  • Yousef Naderi
Assistant Professor of Genetics and Animal Breeding, Islamic Azad University, Astara Branch, Astara, Iran
چکیده [English]

Genomic selection using imputed genotypes can have an important role in increasing economic efficiency andthe genetic improvement of the threshold traits. The objective of this study was to: investigate the accuracy of imputation and to evaluate its effect on area under receiver operating characteristic (AUROC) of threshold BayesA (TBA) and random forest (RF) algorithms for discrete traits with different genomic architectures. Genomic data were simulated to reflect variations in heritability (0.30 and 0.10), number of QTL (108 and 1080) and linkage disequilibrium (low and high) for 27 chromosomes. To simulate a condition close to reality, we randomly masked markers with 50% and 90% missing rate for each scenario; afterwards, missing genotypes were imputed and imputation accuracy was estimated. In the last step, to evaluate the AUROC of TBA and RF, original or imputed genotypes were used. The accuracy of imputation was improved with increasing level of LD and decreased missing rate. The total average of AUROC values were 0.64 and 0.66 when using RF and TBA, respectively. Comparing to original genotypes, using imputed genotypes with 50% and 90% missing rate decreased the average AUROC about 0.013 and 0.02 for RF and 0.0018 and 0.026 for TBA, respectively. Despite the higher AUROC of TBA at different scenarios, RF showed a better performance in large number QTL. Generally, genomic prediction based on imputed genotypes (5K) can be implemented to reduce of the cost of a genomic evaluation.

کلیدواژه‌ها [English]

  • AUROC
  • imputation accuracy
  • Linkage Disequilibrium
  • missing genotype rate
  • simulation
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